Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for reconciling irregularly sampled measurements. We impute measurements at any arbitrary timescale and quantify the uncertainty in the imputations in a principled manner. Specifically, we derive a

Theory and Code

Self Aware Networks OCA First Draft.pdf2025-05-16 | Journal article | Author: Micah Blumberg DOI: 10.6084/m9.figshare.29085134SOURCE-WORK-ID: 29085134Contributors: Micah Blumberg Title: Self Aware Networks: OCA (First Draft) This manuscript, Self Aware Networks: OCA (First Draft), presents the earliest formal articulation of my Self Aware Networks (SAN) framework, a theory of predictive cognition, oscillatory computation, and autonomous network self-reference. The paper was originally published on Figshare

The text discusses using neural networks for trading strategies, highlighting the potential of high returns with significant drawdowns, the importance of predictors, and the use of tools like R and SciLab for training models. It also mentions the limitations of such strategies on smaller timeframes and the need for further development and testing

Recent research shows 1024-layer networks achieve 2x to 50x improvements in goal-conditioned RL. Here's why extreme depth works now, and when you should

NeurIPS Proceedings Search Temporal Abstraction in Temporal-difference Networks Eddie Rafols, Anna Koop, Richard S. Sutton Advances in Neural Information Processing Systems 18 (NIPS 2005) Abstract We present a generalization of temporal-difference networks to include temporally abstract options on the links of the question network. Temporal-difference (TD) networks have been proposed as a way of representing and learning a wide variety of predictions about the interaction between an agent and its environmen

← Back to homepage Building an Image Denoiser with a Keras autoencoder neural network December 20, 2019 by Chris Images can be noisy, and you likely want to have this noise removed. Traditional noise removal filters can be used for this purpose, but they're not data-specific - and hence may remove more noise than you wish, or leave too much when you want it gone. Autoencoders based on neural networks can be used to learn the noise removal filter based on the dataset you wish noise to disappear from. In

In my master’s thesis, I worked with encoder-decoder sequence-to-sequence neural networks. I’m cross-posting this diagram and description I use to describe seq2se

A new accelerator chip called Hiddenite that can achieve state-of-the-art accuracy in the calculation of sparse hidden neural networks with lower computational burdens has now been developed by Tokyo Tech researchers. By

Home AI Weirdness Book: You look like a thing About Janelle Subscribe Search Sign in Sign up AI Weirdness: the strange side of machine learning Tiny neural net Halloween costumes are the best By Janelle Shane On October 28, 2025 - 2 min read I've been experimenting with getting a tiny circa-2015 recurrent neural network to generate Halloween costumes. Running on a single cat hair-covered laptop, char-rnn has no internet training, but learns from scratch to imitate the data I give it. A little while ago I re